What is Digital Twin?
A digital twin technology is composed of three elements – The physical entity, which is the real world, the virtual models and the connected view, the ties to worlds together. In the automobile industry, the product life cycle of the vehicle involves various stages from conceptualization to design, procurement, building stocks, selling, service, and recycling. At each stage, an enormous amount of data is generated. As part of the routine activities, leveraging this data for a faster and cost-effective manufacturing.
The journey of manufacturing a single car is often very long. It takes almost five to six years to produce a single car from design stage to the manufacturing stage. Many prototypes that arrive after five to six years of design, tests and manufacturing are often not approved. Digital twin technology can create a replica of the product and help designers and engineers to simulate and optimize the prototype for various scenarios before committing to a physical prototype, which are very costly.
Digital twin technology can be used to revolutionize the automotive industry to improve product design, manufacturing and maintenance. Digital twins are virtual replicas of physical assets that use real-world to stimulate performance. Let’s discuss the use cases:
Use Cases of Digital Twin in Automotive Industry
Design and Prototyping
In this use case, virtual models are created to test several different materials before building physical prototypes.
Predictive maintenance
After training on a good set of data it can be forecasted when the components might fail and need maintenance so the maintenance can be scheduled before hand.
Production process optimization
Trends and patterns can be identified from the existing data. And efficiency in the production process could be Increased After plugging the bottlenecks.
Managing the supply chain
Supply chains can be optimized after plugging in the bottlenecks. The bottlenecks can be identified by looking at the data collected over a particular set of time.
Vehicle testing
Vehicle testing can be automated by defining a particular set of parameters, such tests can be carried out in virtual environments.
Safety
Digital twin technology can create virtual environments for training and safety to run simulations can help the industry enhance safety standards.
Better Quality Control
Digital twin technology can ensure better quality control by testing the product several times in virtual environments created by digital twin technology. This will greatly improve quality and performance.
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Role of Digital Twin Technology in Automobile Industry
- A digital model, can be created by integrating all the data from the vehicles from the previous generation and concepts of current vehicles with the help of digital twin technology.
- Digital Twin technology can help create better channels of communication between all the stakeholders and can be faster and more interactive.
- It prioritizes data led decision making which can help finalize vehicle concepts after careful consideration.
- Digital twin can help the product development during the simulation testing stage by utilizing the data collected during the life cycle of the product to improve the product quality.
Challenges in Digital Twin Technology
Digital Twin technology has several benefits for revolutionizing the safety standards in automobile industry but these benefits are not without many challenges, let’s discuss few of them:
1. Cost
The foremost challenge to industry wide adoption of digital twin technology in high cost. Adopting digital twin technology requires significant financial investment into acquiring specialized hardware, software and experts.
The cost of implementing or adopting digital twin technology differs from project to project based on it’s complexity. A particular subsystem of a vehicle manufacturing process may require less investment but manufacturing a whole vehicle from design to the end product will require a lot of investment.
2. Technical Issues
a. Accuracy of Data and it’s Quality: The efficiency or quality of digital twin is completely based upon the quality of the data. However, collecting data for digital twin technology ecosystem is a big challenge, gathering data from multiple sources and integrating them together In a proper manner is often a challenging task.
Digital twin with inaccurate, insufficient, or bad quality data may not be able to represent the vehicle. And it may ultimately affect its operational and maintenance activities. A good quality data set is essential for bringing an entire system’s performance to par with expectations with digital twin technology.
b. Scalability: Scalability might not be an issue when you’re working on a particular subsystem of a vehicle, but if a vehicle is to be manufactured completely from scratch, scalability may become a problem. The sheer volume of data that will need to be processed for a whole vehicle to be constructed is a lot, and it can easily become overwhelming. as the data increases in volume and the You know, problem becomes complex. Significant computational resources, which are difficult to scale up may be required. The question now is, Is the industry ready to take on such a challenge?
3. Data Privacy Challenges
Data is the new oil of the century, and the world is getting more sensitive about data, rightfully so. Digital twin technology includes sensors, IoT trackers, etc. And these components have sensitive data. Such as trade secrets, personal data of customers, and proprietary data. It is essential to protect this data from falling into unauthorized wrong hands. Companies must follow strong security and data protection methods like encryption, access controls, and firewalls to avoid data leakages and misuse.
4. Computational Limitations
Automobile manufacturing companies manufacture vehicles in bulk, so it is an obvious reality that a huge amount of data would need to be processed every now and then. More digital twin models. To successfully compute this data and process it in real time, a huge computational capacity would be needed. Investing in high performance computing resources is very expensive and this limits scalability.
Future Trends of Digital Twin in Automobile Manufacturing Industry
In this section, we will discuss the possible future trends in the automobile industry:
- Flexible-cell manufacturing is an alternative in future for the automobile industry, automated guided vehicles will be able to transport car bodies individually to the assembly working stations that are relevant or related to the specific model of the car.
- Digital Twin technology is already being increasingly deployed in helping train the workforce by providing real-time, step-by-step guides on every simple little task like product assembly, design of the components, operation of the machines. Etc
- The digital twin technology can use past operational information to predict when a fault or event might occur. This will help the automobile industry create safer vehicles.
- Every critical part of the vehicle can be monitored by creating a vehicle’s digital twin. Predicting and anticipating breakdowns or malfunctioning by monitoring the component of the vehicles in real world conditions.
- Design and Development: creation of virtual environments can help engineers test and optimize the models of the products safer and efficiently.
- Optimization of the Supply Chains: Digital twin technology can assist in removing bottlenecks to optimize the supply chains and reduce lead times. This will greatly help in reducing the costs and improve overall efficiency.
- Reduced Emissions: Optimization of supply chains, reduction in lead times, improvement in product quality and design can reduce resource consumption and greatly reduce emissions and clamp the environmental footprint of the industry.
How can Neuronimbus help
Neuronimbus can help the automotive industry leverage Digital Twin Technology through its expertise in product development, digital transformation, and cloud engineering.
Being a leading automotive software development company, Neuronimbus enables automakers to design and test virtual vehicle models efficiently. Their business process optimization and data engineering services streamline manufacturing and enhance predictive maintenance. We ensures secure and scalable digital twin adoption, driving efficiency, reducing costs, and optimizing the automotive manufacturing lifecycle.
